Gaming machines, such as slot machines, poker machines, and the like, provide a source of revenue for gaming establishments. A large gaming casino typically employs thousands of gaming machines that can be operated simultaneously. Currently, casino floors include a wide variety of electronic gaming machines, such as video slot machines, poker machines, reel slot machines and other gaming machines. A central line of questioning for casino operational executives is ensuring the correct balance, or mix, of machines exist on the floor, according to some natural division like manufacturer or cabinet type. This has historically done by comparing percentage contribution to some important metric like net win for each category within the division to the percentage of machines within that category. The most common action based on this information is to add additional machines from the category with the highest ratio and removing machines from the category with the lowest. This approach, however, suffers from the implicit assumption that demand for machines of that category is relatively linear in the size of the category, which is often not the case for various classes of machines and play. Thus, while casino operators have access to a wide array of information from various data sources, the challenge is to create context and insights from this information.
Current offerings that are designed to assist casino operators with operational decisioning focus on data and not context or insights. Further, future machine performance of specific machine key performance indicators (KPIs) is most commonly predicted with a linear trend line, generated through a simple statistical process like linear regression on recent historical performance. More advanced methods might utilize basic time series methods to remove seasonality before regressing, but these approaches are extremely simplistic and often yield poor predictions.